A Hybrid K-means Clustering Algorithm Integrating Crow Search Algorithm
The traditional K-means algorithm is sensitive to the selection of initial cluster centers,prone to falling into local optimal solutions,and requires the pre-setting of the number of clusters K,which is often difficult to achieve in practical applications.To overcome these issues,a K-means clustering algorithm that integrates the Crow Search Algorithm is proposed,aiming to address the limitations of the traditional K-means algorithm in clustering analysis.This algorithm leverages the global search capability of the Crow Search Algorithm to automatically determine the optimal number of clusters K,thereby improving the quality and efficiency of clustering.Experiments conducted on the Seeds dataset,calculating evaluation indices such as the Calinski-Harabasz index,have found that the clustering effect of this algorithm is significantly superior to that of the traditional K-means algorithm.
K-means algorithmcrow search algorithmclusteringCalinski-Harabasz index
NETL
NSTL
万方数据
维普
